A bigram is a term used in the field of natural language processing (NLP) and information retrieval to describe a sequence of two adjacent words in a given text or corpus. The term "bi" signifies the number two, indicating that a bigram focuses on pairs of words rather than individual words or larger units of text.
In the context of NLP, a bigram can be seen as a simplistic representation of language, as it allows for the analysis and examination of the relationship between adjacent words. By considering bigrams, patterns or tendencies in language usage can be identified, which can be valuable for various NLP tasks like language modeling, text classification, and information retrieval.
Bigrams play a crucial role in tasks such as machine translation, part-of-speech tagging, and sentiment analysis, as they provide additional context and understanding of a given text. By analyzing bigrams, one can capture information about collocations, word associations, and statistical properties of language use.
In practice, bigrams can be generated by sliding a window of two adjacent words along a text, collecting each pair encountered. For example, in the sentence "The quick brown fox jumps over the lazy dog," the bigrams would include "The quick", "quick brown", "brown fox", "fox jumps", "jumps over", "over the", "the lazy", and "lazy dog".
Overall, bigrams offer a useful tool for analyzing language patterns and relationships, facilitating various NLP tasks that require an understanding of adjacent word sequences within a given text or corpus.
